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Using ELECTRE to analyse the behaviour of economic agents

  • Gerarda Fattoruso
  • Gabriella MarcarelliEmail author
  • Maria Grazia Olivieri
  • Massimo Squillante
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Abstract

According to behavioural finance, economic agents display cognitive bias, heuristics and emotional factors that generate preferences which systematically violate the rationality assumptions of the normative model of classical decision theory. Rather than maximizing the expected utility, representing the optimal choice, they attempt to accept a satisfactory solution. Morton and Fasolo (J Oper Res Soc 60:268–275, 2009) outlined some behavioural findings relevant to the practice of multicriteria approach. In this paper, we propose a multicriteria model for analysing some experiments proposed by Kahneman and Tversky (Econometrica 47:263–29 l, 1979). Our aim is to verify whether a multicriteria tool reduces or minimizes cognitive biases. We focus on ELECTRE due to its main features: it accepts the violation of some mathematical axioms. By a simulation study, we represent a set of prospects by means of decision matrices: the prospects are considered as alternatives, the events as criteria, the probabilities of events as the weights assigned to criteria. Then, we apply ELECTRE to verify whether the preference ranking among the alternatives confirms the results obtained by Kahneman–Tversky, that is, whether it is able to describe the emotional behaviours of economic agents.

Keywords

MCDM ELECTRE Rationality Prospect theory Behavioural finance 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Allais M (1953) Le Comportement de l’Homme Rationnel devant le Risque: Critique des Postulats et Axiomes de l’Ecole Americaine. Econometrica 21(4):503–546MathSciNetCrossRefGoogle Scholar
  2. Anscombe FJ, Aumann R (1963) Definition of subjective probability. Ann Math Stat 34(1):199–205MathSciNetCrossRefGoogle Scholar
  3. Ellsberg D (1961) Risk, ambiguity and savage axioms. Q J Econ 75(4):643–669MathSciNetCrossRefGoogle Scholar
  4. Ferretti R, Rubaltelli E, Rumiati R (2011) La mente finanziaria. Economia e psicologia al servizio dell’investitore. Il Mulino, Fiesole, pp 1–310Google Scholar
  5. Figueira J, Roy B (2002) Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. Eur J Oper Res 139:317–326CrossRefGoogle Scholar
  6. Figueira J, Greco S, Slowinski R (2009) Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method. Eur J Oper Res 195(2):460–486MathSciNetCrossRefGoogle Scholar
  7. Ishizaka A, Nemery P (2013) Multi-criteria decision analysis methods and software. Wiley, New YorkCrossRefGoogle Scholar
  8. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291MathSciNetCrossRefGoogle Scholar
  9. Kahneman D, Tversky A (1986) Rational choice and the framing of decisions. J Bus 59(4):251–278zbMATHGoogle Scholar
  10. Korhonen P, Moskowitz H, Wallenius J (1990) Choice behavior in interactive multiple criteria decision making. Ann Oper Res 23:161–179MathSciNetCrossRefGoogle Scholar
  11. Luce RD, Krantz DH (1971) Conditional expected utility. Econometrica 39:253–271MathSciNetCrossRefGoogle Scholar
  12. Morton A, Fasolo B (2009) Behavioural decision theory for multi-criteria decision analysis: a guided tour. J Oper Res Soc 60:268–275CrossRefGoogle Scholar
  13. Roy B (1990) The outranking approach and the foundations of ELECTRE methods. In: Bana e Costa CA (ed) Readings in multiple criteria decision aid. Springer, Berlin, pp 155–183CrossRefGoogle Scholar
  14. Roy B (1991) The outranking approach and thinks of ELECTRE methods. Theor Decis 31:49–73CrossRefGoogle Scholar
  15. Roy B, Mousseau V (1996) A theoretical framework for analysing the notion of relative importance of criteria. J Multi-Criteria Decis Anal 5(2):145–159CrossRefGoogle Scholar
  16. Saaty T (1980) The analytic hierarchy process. McGraw-Hill, New YorkzbMATHGoogle Scholar
  17. Saaty TL (1986) Axiomatic foundation of the analytic hierarchy process. Manag Sci 32:841–855MathSciNetCrossRefGoogle Scholar
  18. Salminen P (1994) Solving the discrete multiple criteria problem using linear prospect theory. Eur J Oper Res 72:146–154CrossRefGoogle Scholar
  19. Savage LJ (1954) The foundation of statistics. Wiley, New YorkGoogle Scholar
  20. Shefrin H (2002) Behavioral decision making, forecasting, game theory, and role-play. Int J Forecast 18(3):375–382CrossRefGoogle Scholar
  21. Simon HA (1990) Bounded rationality. In: Eatwell J, Milgate M, Newman P (eds) Utility and probability. Palgrave Macmillan, London, pp 15–18CrossRefGoogle Scholar
  22. Slovic P, Lichtenstein S (1971) Comparison of Bayesian and regression approaches to the study of information processing in judgment. Organ Behav Hum Perform 6(6):649–744CrossRefGoogle Scholar
  23. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157):1124–1131CrossRefGoogle Scholar
  24. Tversky A, Kahneman D (1991) Loss aversion in riskless choice: a reference-dependent model. Quart J Econ 106(4):1039–1061CrossRefGoogle Scholar
  25. von Neumann J, Morgenstern O (1947) Theory of games and economic behavior, 2nd edn. Princeton University Press, PrincetonzbMATHGoogle Scholar
  26. Yücel MG, Görener A (2016) Decision making for company acquisition by ELECTRE method. Int J Supply Chain Manag 5(1):75–83Google Scholar
  27. Zweig J (2007) Your money and your brain: how the new science of neuroeconomics can help make you rich. Simon & Schuster, New YorkCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of SannioBeneventoItaly

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